The Informatics Core (IC) will translate patient data on chronic lower back pain (cLBP) collected by UCSF REACH into translational models of clinical intelligence that improve cLBP outcomes. By coupling UCSF's extensive clinical experience with this second-to-none infrastructure to perform AI research based on EHR data, the IC has the potential to produce innovative collaborative research that significantly improves cLBP outcomes for patients. The overarching goal of IC is to work in symbiosis with other UCSF REACH Research and Clinical cores to develop novel AI methodologies for interpreting imaging and other Electronic Health Record (EHR) data to significantly improve cLBP care. In order to enable the full utilization of data collected by UCSF REACH, IC will aid in the development and clinical application of statistical and machine learning methods with the following specific aims. In in Aim 1, to identify patient subgroups based on cLBP phenotypes, we will work with the Reall other cores to use the existing curated EHR and newly collected data from cLBP patients to better understand cLBP disease pathways. To analyze trade-offs and synergies between treatment objectives, IC will apply canonical correlation analysis to discover clinical insights potential synergies and trade-offs between various treatment outcomes (pain, physical and psychosocial disability metrics) to develop machine learning methods for predicting patient-specific treatment response. Finally, IC will apply state-of-the art convolutional neural network techniques in predicting clinically relevant outcomes from medical imaging to optimize this clinical insight and personalize treatment plans for patients.